期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 400, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2022.115501
关键词
Nonlinear electro-elasticity; Constitutive modeling; Physics-augmented machine learning; Electro-active polymers; Homogenization
资金
- Deutsche Forschungsgemeinschaft (DFG- German Research Foundation) [460684687]
- Graduate School of Computational Engineering within the Centre of Computational Engineering at the Technical University of Darmstadt
- Fundacion Seneca, Region de Murcia (Spain) [460684687]
- Fundacion Seneca (Murcia, Spain) [21132/SF/19]
- [20911/PI/18]
- [PID2021-125687OA-I00]
In this work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. The model formulates an internal energy density as a convex neural network using different sets of invariants as inputs. It demonstrates applicability and versatility through calibration on different materials data and effective modeling of composite materials.
In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of invariants as inputs, an internal energy density is formulated as a convex neural network. In this way, the model fulfills the polyconvexity condition which ensures material stability, as well as thermodynamic consistency, objectivity, material symmetry, and growth conditions. Depending on the considered invariants, this physics-augmented machine learning model can either be applied for compressible or nearly incompressible material behavior, as well as for arbitrary material symmetry classes. The applicability and versatility of the approach is demonstrated by calibrating it on transversely isotropic data generated with an analytical potential, as well as for the effective constitutive modeling of an analytically homogenized, transversely isotropic rank-one laminate composite and a numerically homogenized cubic metamaterial. These examinations show the excellent generalization properties that physics-augmented neural networks offer also for multi-physical material modeling such as nonlinear electro-elasticity.(c) 2022 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据